Aggregate functions perform a computation against a set of values to generate a single result. For example, you could use an aggregate function to compute the average (mean) order over a period of time. Aggregations can be applied as standard functions or used as part of a transform step to reshape the data.
Aggregate across an entire column:
derive type:single value:AVERAGE(Scores)
Output: Generates a new column containing the average of all values in the Scores column.
pivot value: AVERAGE(Score) limit: 1
Output: Generates a single-column table with a single value, which contains the average of all values in the Scores column. The limit defines the maximum number of columns that can be generated.
NOTE: When aggregate functions are applied as part of a pivot transform, they typically involve multiple parameters as part of an operation to reshape the dataset. See below.
Aggregate across groups of values within a column:
Aggregate functions can be used with the pivot transform to change the structure of your data. Example:
pivot group: StudentId value: AVERAGE(Score) limit: 1
In the above instance, the resulting dataset contains two columns:
studentId- one row for each distinct student ID valueaverage_Scores- average score by each student (studentId)
NOTE: You cannot use aggregate functions inside of conditionals that evaluate to true or false.
A Pivot Table transformation can include multiple aggregate functions and group columns from the pre-aggregate dataset.
For more information on the transform, see Pivot Data.
NOTE: Null values are ignored as inputs to these functions.
These aggregate functions are available:
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